Lung cancer is responsible for a significant percentage of all cancer related deaths, and is generally associated with grim prognosis unless diagnosed early. Early detection of pulmonary nodules via screening procedure is essential to enable clinicians determine the patient's treatment roadmap. Low dose computed tomography (LDCT) scan is the de-facto standard for lung cancer screening, and lacks an automated lung nodule characterization framework to encompass the potential of three dimensional LDCT imaging for lung cancer screening.
A fully automated lung cancer screening procedure relies on three distinct steps. First, an automated lung nodule detection framework identifies the location of a nodule. This is an active area of research which has received significant attention in the recent years. Second, an automatic lung nodule segmentation procedure identifies the nodule boundary. Finally, the segmented nodule is analyzed via machine learning based methods to characterize the nodule as a benign, or a potentially malignant case. Solutions are lacking, however, as to issues in extracting features for characterization.
Automated nodule segmentation from three-dimensional (3D) chest computed tomography (CT) is in general a difficult problem since the appearance and morphology of a nodule could vary considerably depending on its type (e.g., solid or semi-solid) or based on the stage of the disease. Moreover, noise, reconstruction artefact, and presence of other pulmonary structures such as vessels, fissures, among others, complicate the segmentation problem. It is desired to address these issues to handle noise and imaging artifacts, and should demonstrate ability to suppress the non-nodule structures during segmentation. In addition, the following disclosure will beneficially address learned pulmonary nodule features in a model based, energy minimization segmentation problem. The details are as follows.